How can I optimize thread pools in Python?

How can I optimize thread pools in Python?

To optimize thread pools in Python, you can use the concurrent.futures.ThreadPoolExecutor class. It allows you to manage a pool of threads efficiently.

What factors should I consider when deciding the number of threads?

You should consider the nature of the tasks (I/O-bound vs CPU-bound), the number of available CPU cores, and the overhead of context switching.

Can you give an example of using ThreadPoolExecutor?

Sure! Here's a simple example:

from concurrent.futures import ThreadPoolExecutor

def task(n):
    return n * n

with ThreadPoolExecutor(max_workers=5) as executor:
    results = list(executor.map(task, range(10)))
print(results)

How do I handle exceptions in a thread pool?

You can handle exceptions by using a try-except block within the task function or by checking the results after execution.